Electrical Capacitance Volume Tomography (ECVT) is a non-invasive imaging modality. Its applications span an array of industries. Most notably, ECVT is applicable to multiphase flow applications commonly employed in many industrial processes. ECVT is often the technology of choice due to its advantages of high imaging speed, scalability to different process vessels, flexibility, and safety. In ECVT, sensor plates are distributed around the circumference of the column, object or vessel under interrogation. The number of sensor plates may be increased to acquire more capacitance data. However, increasing the number of sensor plates reduces the area of each sensor plate accordingly. A limit exists on the minimum area of a sensor plate for a given column diameter, thus limiting the maximum number of plates that can be used in an ECVT sensor. This limit is dictated by the minimum signal-to-noise ratio requirement of the data acquisition system. Since ECVT technology is based on recording changes in capacitance measurements induced by changes in dielectric distribution (i.e., phase distribution), and the capacitance level of a particular sensor plate combination is directly proportional to the area of the plates, minimum signal levels are needed to provide sufficiently accurate measurements. These considerations dictate the required minimum sensor plate dimensions. This limitation on the minimum size of the sensor plates, while increasing the number of available sensor plates in an ECVT sensor, is one of the main hurdles in achieving a high resolution imaging system.
To overcome this challenge, the concept of Adaptive Electrical Capacitance Volume Tomography (AECVT) was recently developed, whereby the number of independent capacitance measurements is increased through the use of reconfigurable synthetic sensor plates composed of many smaller sensor plates (constitutive segments). These synthetic sensor plates maintain the minimum area for a given signal-to-noise ratio (SNR) and acquisition speed requirements while allowing for many different combinations of (synthetic) sensor plates in forming a sensor plate pair.
Electrical Capacitance Tomography (ECT) is the reconstruction of material concentrations of dielectric physical properties in the imaging domain by inversion of capacitance data from a capacitance sensor. Electrical Capacitance Volume Tomography or ECVT is the direct 3D reconstruction of volume concentrations or physical properties in the imaging domain utilizing 3D features in the ECVT sensor design. ECVT technology is described in U.S. Pat. No. 8,614,707 to Warsito et al. which is hereby incorporated by reference.
Adaptive Electrical Capacitance Volume Tomography (AECVT) provides higher resolution volume imaging of capacitance sensors based on different levels of activation levels on sensor plate segments. AECVT is described in U.S. Patent Application Publication US2013/0085365 A1 to Marashdeh et al. which is hereby incorporated by reference.
In ECT, ECVT, or AECVT, the capacitance measurement between sensor plates is also related to the effective dielectric content between that plate pair. The SART method can be extended to all measurements of ECT, ECVT, or AECVT sensors, thus providing a high resolution visual representation of each phase through image reconstruction.
Electrical capacitance sensors are used for non-invasive imaging by distributing the electric field inside the imaging domain in 3D. ECVT sensors enable sensitivity variation in the imaging domain that can utilize different plate shapes and distributions to target a volume for imaging. They exhibit flexibility for fitting around different sizes and geometries and are scalable to different sizes. Capacitance sensors so far have been focused on being passively applied around a geometry. In such arrangements, the capacitance plates are designed to fit around the targeted geometry and the sensor shape is recorded for image reconstruction purposes. In the present invention, capacitance sensors are designed with a smart feature that enables the sensor to detect and quantify the geometry it is placed around. Capacitance sensors in this case are developed from flexible materials that can be used for imaging volumes of different shapes or sizes. The smart capacitance sensor is able to detect the shape and size of the volume it is placed around formulate a sensitivity matrix for such volume, acquire capacitance measurements, and provide reconstructed images. Each pair of inner geometry sensor plates detect a capacitance signal that has information on how much the sensor stretched in that region. The difference in stretch-ability around the geometry is used to infer the shape of the geometry. The total stretch of the sensor tells the volumes. For example, in FIG. 5b, you can see the sensor stretching more where the geometry is larger. The geometry plates would be able to detect this difference and infer a shape.
The present invention also relates to stretchable, flexible, wearable, and modular capacitance sensors for applications that involve tomography imaging. In prior technologies (patent application Ser. No. 13/965,636), stretchable, flexible, wearable, and modular capacitance sensors are designed based on 2D stretching of the sensor area. In the present invention, the stretching is based on shrinking or expanding a third (depth) dimension that is also equipped with capacitance or other sensors to detect and quantify the distance of sensor plates from each other. This feature enables the sensor to measure and quantify the shape and size of the volume it is placed around. The inner folded regions have plates on the surfaces. When the sensor stretches, the distance between those plates grows larger and the capacitance between them becomes less. Thus, the measured capacitance between those inner plates is used to measure stretch-ability in the locality of those inner plates. Different stretch-ability measurements around the object is used to infer a geometry from which a volume is also calculated.
In the present invention, curved plates are also allowed to reduce hot spots of sensitivity in the imaging domain. Those hot spots complicate the image reconstruction process.